It has become common in recent years for vehicles to be equipped with navigation devices, either in the form of portable navigation devices (PNDs) that can be removably positioned within the vehicle or systems that are integrated into the vehicle. These navigation devices comprise a means for determining the current position of the device; typically a global navigation satellite system (GNSS) receiver, such as GPS or GLONASS. It will be appreciated, however, that other means may be used, such as using the mobile telecommunications network, surface beacons or the like.
Navigation devices also have access to a digital map representative of a navigable network on which the vehicle is travelling. The digital map (or mathematical graph, as it is sometimes known), in its simplest form, is effectively a database containing data representative of nodes, most commonly representative of road intersections, and lines between those nodes representing the roads between those intersections. In more detailed digital maps, lines may be divided into segments defined by a start node and end node. These nodes may be “real” in that they represent a road intersection at which a minimum of 3 lines or segments intersect, or they may be “artificial” in that they are provided as anchors for segments not being defined at one or both ends by a real node to provide, among other things, shape information for a particular stretch of road or a means of identifying the position along a road at which some characteristic of that road changes, e.g. a speed limit. In practically all modern digital maps, nodes and segments are further defined by various attributes which are again represented by data in the database. For example, each node will typically have geographical coordinates to define its real-world position, e.g. latitude and longitude. Nodes will also typically have manoeuvre data associated therewith, which indicate whether it is possible, at an intersection, to move from one road to another; while the segments will also have associated attributes such as the maximum speed permitted, the lane size, number of lanes, whether there is a divider in-between, etc. For the purposes of this application, a digital map of this form will be referred to as a “standard map”.
Navigation devices are arranged to be able to use the current position of the device, together with the standard map, to perform a number of tasks, such as guidance with respect to a determined route, and the provision of traffic and travel information relative to the current position or predicted future position based on a determined route.
It has been recognised, however, that the data contained within standard maps is insufficient for various next generation applications, such as highly automated driving in which the vehicle is able to automatically control, for example, acceleration, braking and steering without input from the driver, and even fully automated “self-driving” vehicles. For such applications, a more precise digital map is needed. This more detailed digital map typically comprises a three-dimensional vector model in which each lane of a road is represented separately, together with connectivity data to other lanes. For the purposes of this application, a digital map of this form will be referred to as a “planning map” or “high definition (HD) map”.
An representation of a portion of a planning map is shown in FIG. 1, wherein each line represents the centreline of a lane. FIG. 2 shows another exemplary portion of a planning map, but this time overlaid on an image of the road network. The data within these maps is typically accurate to within a meter, or even less, and can be collected using various techniques.
One exemplary technique for collecting the data to build such planning maps is to use mobile mapping systems; an example of which is depicted in FIG. 3. The mobile mapping system 2 comprises a survey vehicle 4, a digital camera 40 and a laser scanner 6 mounted on the roof 8 of the vehicle 4. The survey vehicle 2 further comprises a processor 10, a memory 12 and a transceiver 14. In addition, the survey vehicle 2 comprises an absolute positioning device 2, such as a GNSS receiver, and a relative positioning device 22 including an inertial measurement unit (IMU) and a distance measurement instrument (DMI). The absolute positioning device 20 provides geographical coordinates of the vehicle, and the relative positioning device 22 serves to enhance the accuracy of the coordinates measured by the absolute positioning device 20 (and to replace the absolute positioning device in those instances when signals from the navigation satellites cannot be received). The laser scanner 6, the camera 40, the memory 12, the transceiver 14, the absolute positioning device 20 and the relative positioning device 22 are all configured for communication with the processor 10 (as indicated by lines 24). The laser scanner 6 is configured to scan a laser beam in 3D across the environment and to create a point cloud representative of the environment; each point indicating the position of a surface of an object from which the laser beam is reflected. The laser scanner 6 is also configured as a time-of-flight laser range-finder so as to measure a distance to each position of incidence of the laser beam on the object surface.
In use, as shown in FIG. 4, the survey vehicle 4 travels along a road 30 comprising a surface 32 having road markings 34 painted thereon. The processor 10 determines the position and the orientation of the vehicle 4 at any instant of time from position and orientation data measured using the absolute positioning device 20 and the relative positioning device 22, and stores the data in the memory 12 with suitable timestamps. In addition, the camera 40 repeatedly captures images of the road surface 32 to provide a plurality of road surface images; the processor 10 adding a timestamp to each image and storing the images in the memory 12. The laser scanner 6 also repeatedly scans the surface 32 to provide at least a plurality of measured distance values; the processor adding a timestamp to each distance value and stores them in the memory 12. Examples of the data obtained from the laser scanner 6 are shown in FIGS. 5 and 6. FIG. 5 shows a 3D view, and FIG. 6 shows a side view projection; the colour in each picture being representative of the distance to the road. All the data obtained from these mobile mapping vehicles can be analysed and used to create planning maps of the portions of the navigable (or road) network travelled by the vehicles.
It has been recognised by the Applicant that in order to use such planning maps for highly and fully automated driving applications, it is necessary to know the position of a vehicle relative to the planning map to a high degree of accuracy. The traditional technique of determining the current location of a device using navigation satellites or terrestrial beacons provides an absolute position of the device to an accuracy of around 5-10 meters; this absolute position is then matched to a corresponding position on the digital map. While this level of accuracy is sufficient for most traditional applications, it is not sufficiently accurate for next generations applications, where positions relative to the digital map are required at sub-meter accuracy even when travelling at high speeds on the road network. An improved positioning method is therefore required.